Methods, systems, and products for personalized monitoring of data

ABSTRACT

A personalized data monitoring service is provided to users. Data from a user&#39;s devices is collected and compared to user-defined rules and to ranges. Notification messages may be sent to notify of the data. Data labels may be added to explain the data and any abnormal condition.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/005,044 filed Jan. 25, 2016 and since issued as U.S. Pat. No.9,560,151, which is a continuation of U.S. application Ser. No.13/691,797 filed Dec. 2, 2012 and since issued as U.S. Pat. No.9,268,860, with both applications incorporated herein by reference intheir entireties.

BACKGROUND

Massive amounts of data are being generated and collected. Each visit toa doctor generates health-related data. Smart phones generate data basedon usage and location. Even more data is being generated by stand alonedigital devices, communications network devices, home automationdevices, security systems, and banking transactions. No individual usercan hope to monitor all the data being generated.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The features, aspects, and advantages of the exemplary embodiments arebetter understood when the following Detailed Description is read withreference to the accompanying drawings, wherein:

FIG. 1 is a simplified schematic illustrating an environment in whichexemplary embodiments may be implemented;

FIG. 2 is a more detailed block diagram illustrating the operatingenvironment, according to exemplary embodiments;

FIG. 3 is a detailed diagram illustrating a profile, according toexemplary embodiments;

FIG. 4 is a schematic illustrating data queries, according to exemplaryembodiments;

FIG. 5 is a schematic illustrating a monitoring service, according toexemplary embodiments;

FIG. 6 is a schematic illustrating a client interface for the monitoringservice, according to exemplary embodiments;

FIG. 7 is a schematic illustrating a service ecosystem, according toexemplary embodiments;

FIGS. 8-9 are schematics illustrating personalized rules, according toexemplary embodiments;

FIG. 10 is a schematic illustrating multiple data ranges, according toexemplary embodiments;

FIGS. 11-13 are graphical illustrations of the client interface,according to exemplary embodiments;

FIGS. 14-15 are more schematics illustrating the profile, according toexemplary embodiments;

FIGS. 16-17 are flowcharts illustrating a method or algorithm for datamonitoring, according to exemplary embodiments; and

FIGS. 18-19 depict still more operating environments for additionalaspects of the exemplary embodiments.

DETAILED DESCRIPTION

The exemplary embodiments will now be described more fully hereinafterwith reference to the accompanying drawings. The exemplary embodimentsmay, however, be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein. Theseembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the exemplary embodiments to those ofordinary skill in the art. Moreover, all statements herein recitingembodiments, as well as specific examples thereof, are intended toencompass both structural and functional equivalents thereof.Additionally, it is intended that such equivalents include bothcurrently known equivalents as well as equivalents developed in thefuture (i.e., any elements developed that perform the same function,regardless of structure).

Thus, for example, it will be appreciated by those of ordinary skill inthe art that the diagrams, schematics, illustrations, and the likerepresent conceptual views or processes illustrating the exemplaryembodiments. The functions of the various elements shown in the figuresmay be provided through the use of dedicated hardware as well ashardware capable of executing associated software. Those of ordinaryskill in the art further understand that the exemplary hardware,software, processes, methods, and/or operating systems described hereinare for illustrative purposes and, thus, are not intended to be limitedto any particular named manufacturer.

As used herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless expressly stated otherwise. Itwill be further understood that the terms “includes,” “comprises,”“including,” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. It will be understood thatwhen an element is referred to as being “connected” or “coupled” toanother element, it can be directly connected or coupled to the otherelement or intervening elements may be present. Furthermore, “connected”or “coupled” as used herein may include wirelessly connected or coupled.As used herein, the term “and/or” includes any and all combinations ofone or more of the associated listed items.

It will also be understood that, although the terms first, second, etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first device could be termed asecond device, and, similarly, a second device could be termed a firstdevice without departing from the teachings of the disclosure.

FIG. 1 is a simplified schematic illustrating an environment in whichexemplary embodiments may be implemented. FIG. 1 illustrates amonitoring server 20 collecting data 22 in a cloud computing environment24. The monitoring server 20 provides a monitoring service 26 that sendsan alert 28 when the data 22 satisfies some rule 30 and/or threshold 32.The data 22 may be any information that a user wishes monitored, such ascomputer system information, computer application information,electronic health information, location information, and environmentalinformation. The data 22, as a simple example, may be generated by adata server that sends the data 22 asynchronously, in a one directionoutgoing broadcast flow, to the monitoring server 20. The data server,in other words, may not respond to a network query. An example of anasynchronous data server is a temperature monitoring device thatbroadcasts the temperature measurement periodically, such as once every10 seconds. The user may establish a profile 34 that specifies what data22 is collected and how the data 22 is compared to the rule 30 and/orthreshold 32. Whatever the data 22, the monitoring service 26 determineswhen the data 22 is abnormal, according to the user's self-defined rule30 and/or threshold 32. When the data 22 satisfies the user's rule 30and/or threshold 32, the monitoring service 26 may send the alert 28.The alert 28 may contain information on the data 22 that triggered therule 30, and information associated with the data 22. The alert 28 issent to a notification address 36, again as determined by the user'sprofile 34. That is, the monitoring service 26 allows the user to definewho (or what notification addresses 36) is informed when the data 22 isdetermined abnormal, exceptional, or outside desired ranges. FIG. 1, forsimplicity, illustrates the alert 28 routing to the notification address36 associated with any notification device 38, such as a user's smartphone 40.

Exemplary embodiments thus provide the cloud-based monitoring service26. The user's personal data 22 is stored and compared to the user'sself-defined rules 30 for triggering the alert 28. The alert 28 may besent to any recipient(s) the user desires. Exemplary embodiments mayeven share the data 22 with any recipient, such as local and remote caregivers and other service providers. The monitoring service 26 thusallows the user to keep trusted ones informed of any personal data, suchas medical, security, and financial information. The monitoring service26, however, may also track and inform of any data, such as businessinformation, computer system information, and computer networkinformation. The monitoring service 26 may even provide predefined toolsfor analyzing the data 22, such as statistical analysis that summarizesthe data 22.

The data 22 may be generated by any device. The data 22, for example,may be collected by a networked device that captures the data 22 from astand-alone networked or non-networked device. The data 22 may also becollected by, or transmitted from, a device with localized communicationlinks, and even a complex home automation system with communicationlinks to the Internet. The monitoring server 20 may even collect thedata 22 from third-party service providers, such communications,banking, health care, and security services. Some examples of the data22 generated by devices include computer, computer application, landlineor wireless telecommunications network equipment, commercial or DC powerlevel and frequency monitor, audio noise monitor, radio frequencywideband or narrow-band noise monitor, optoelectronic signal monitor,temperature station, smoke/CO2 detectors, personal weather station,personal emergency alert device, perimeter video surveillance camera,blood pressure machine, water leak detector, wireless pill bottle,wireless armband collecting health and fitness data, home automationsystem, medical monitoring devices such as wireless diabetics meters,smart phone data usage and battery monitoring applications, and GPSlocation application.

The monitoring service 26 thus helps manage the user's business andpersonal data 22. As more and more devices collect the data 22, thevolume of the data 22 grows, especially in today's mobile environment.Monitoring and analysis of that data 22 can be an overwhelming task.Exemplary embodiments, though, automate the monitoring and analysisfunctions, thus providing the user with a simple and configurableservice. Much of the data 22 may be routine and within normalexpectations. However, one person's “normal” range could be “abnormal”to another person. Exemplary embodiments thus permit the user toself-define the data 22 they wish to be monitored, along with the rules30 and/or thresholds 32 that determine when the alert 28 is sent. Thatis, the user may configure their own ranges of abnormal or exceptionaldata 22. Exemplary embodiments provide a turn-key monitoring service 26that collects, correlates, and even translates the user's personal data22 into actionable information specific to the user's interests.

The monitoring service 26 is rule-based. The monitoring service 26 isdeployed to provide the user with rule-based features that identify thedata 22 that needs attention by one or more recipients. The monitoringservice 26 may provide access to an integrated view of the user's owndata 22. The user may update the notification addresses 36 in the user'sprofile 34 using this integrated solution. The user may define the rules30 and thresholds 32, thus allowing the user to self-determine what dataconditions require the alert 28. The user may even manage a recipientlist of the notification addresses 36, thus defining different messageformats for different recipients. Some recipients may receive a simpletext message, while other recipients may receive pictures and even videodata. Distribution lists may even be defined, thus alerting somerecipients via email, TWITTER®, text message, instant messaging, voicemessage, or other account. The rules 30 may even be defined to suppressspurious false alarms.

The user's profile 34 is self-configured. The user determines what data22 is collected, and how the collected data 22 is monitored. The usermay thus construct and personalize the rules 30 that translateexceptional data 22 into the personalized, actionable alert 28. Both therules 30 and the threshold 32 may have dependence on time of day, andcalendar dependence by day of week and month of year, and/or season ofthe year. The cloud-based monitoring server 20 collects, stores, andprocesses the user's desired data 22. The monitoring server 20 may alsohave a web-based interface that permits the user to manage the profile34 and to view a history of the alerts 28 previously sent to recipients.

FIG. 2 is a more detailed block diagram illustrating the operatingenvironment, according to exemplary embodiments. Here the monitoringserver 20 communicates with a process server 40 via the cloud computingenvironment 24. The cloud computing environment 24 is illustrated as acommunications network 42. The process server 40 executes any process 44that generates the data 22. The monitoring server 20 queries the processserver 40 to obtain the data 22. The monitoring server 20 has aprocessor 50 (e.g., “μP”), application specific integrated circuit(ASIC), or other component that executes a monitoring algorithm 52stored in a memory 54. The monitoring algorithm 52 instructs theprocessor 50 to query the process server 40 for the data 22 specified bythe user's profile 34. The user's profile 34 may be retrieved from adatabase 56 of profiles. FIG. 2 illustrates the database 56 of profilesas being locally stored in the memory 54 of the monitoring server 20.The database 56 of profiles, however, may be remotely stored andaccessed from any network location in the communications network 42.Once the data 22 is retrieved, the monitoring algorithm 52 instructs theprocessor 50 to compare the data 22 to the rules 30 and thresholds 32defined in the user's profile 34. If the data 22 satisfies the rules 30and/or thresholds 32, the monitoring algorithm 52 instructs theprocessor 50 to execute an associated action 58. The action 58, forexample, may include sending the alert 28 to the notification address 36specified by the user's profile 34.

The data 22 may also be received from a data server 55. The data server55 receives and accumulates the data 22 from any source. The data server55 may then send or transfer the data 22 to the monitoring server 20.The data 22 may be periodically sent according to date and time, or thedata 22 may be randomly sent when conditions warrant. When themonitoring server 20 receives the data 22 from the data server 55, themonitoring algorithm 52 may again instruct the processor 50 to comparethe data 22 to the rules 30 and thresholds 32 defined in the user'sprofile 34. If the data 22 from the data server 55 satisfies the rules30 and/or thresholds 32, the associated action 58 may be executed, suchas sending the alert 28. Here, though, exemplary embodiments may analyzedifferent combinations of the data 22 received from the process server40 and the data server 55. Different time periods, for example, may beestablished, such that the process server 40 and the data server 55 maybe queried at different times of day or according to different periodsof time. The rule 30 may require different percentages of the data 22 beretrieved and combined from the process server 40 and from the dataserver 55. That is, the monitoring algorithm 52 may retrieve and combinedifferent combinations of the data 22 from the process server 40 andfrom the data server 55, according to the user's rule 30. The rule 30may also require a historical evaluation of the data 22 from the processserver 40 and from the data server 55, perhaps according to differentintervals of time. As an example, the rule 30 may require combiningthree (3) instances of high values of the data 22 from the processserver 40 occurring within the past hour with two (2) instances of highvalues from the data server 55 occurring within the past hour. Anotherexample of the rule 30, a single occurrence of the data 22 within thepast fifteen (15) minutes from the data server 55 may be combined withthree (3) occurrences of the data 22 from the process server 40occurring within the last two (2) hours.

Exemplary embodiments, though, may be applied regardless of networkingenvironment. As the above paragraphs mentioned, the communicationsnetwork 42 may be a wireless network having cellular or WI-FI®capabilities. The communications network 42, however, may also operateusing any other frequency or standard, such as the BLUETOOTH® standardor the Internet Protocol (IP). The communications network 42, however,may be a cable network operating in the radio-frequency domain and/orthe Internet Protocol (IP) domain. The communications network 42,however, may also include a distributed computing network, such as theInternet (sometimes alternatively known as the “World Wide Web”), anintranet, a local-area network (LAN), and/or a wide-area network (WAN).The communications network 42 may include terrestrial or satellite radiofrequency transmission, coaxial cables, copper wires, fiber optic lines,and/or hybrid-coaxial lines. The communications network 42 may eveninclude wireless portions utilizing any portion of the electromagneticspectrum and any signaling standard (such as the IEEE 802 family ofstandards, GSM/CDMA/TDMA or any cellular standard, and/or the ISM band).The communications network 42 may even include powerline portions, inwhich signals are communicated via electrical wiring. The conceptsdescribed herein may be applied to any wireless/wireline communicationsnetwork, regardless of physical componentry, physical configuration, orcommunications standard(s).

FIG. 3 is a more detailed diagram illustrating the profile 34, accordingto exemplary embodiments. If the user wishes to subscribe to themonitoring service 26, the user may be prompted to define the profile34. The user, for example, completes some registration or set-up phasethat determines what data 22 the user wishes collected and monitored.The user selects what data 22, from what devices, is monitored. Theprofile 34, for example, may store a table 60 that lists deviceidentifiers 62. Each monitored device is uniquely identified by itsassociated device identifier 62. The user selects the device identifier62 that corresponds to the device to be monitored.

FIG. 3 also illustrates query addresses 64. Once the user selects orenters the device identifier 62, the monitoring server 20 needs to knowfrom where the data 22 is retrieved. The profile 34 may thus alsospecify the query address 64 associated with each device identifier 62.The table 60 maps, relates, or otherwise associates the deviceidentifier 62 to its corresponding query address 64. The query address64 is any network address (such as an Internet Protocol address) fromwhich the data 22 is retrieved, such as the process server 40. Theprocess server 40 executes some process 44 that stores the data 22generated by the corresponding device identifier 62. The profile 34 maythus be populated with the device identifiers 62 to be monitored andtheir corresponding query addresses 64.

FIG. 4 is a schematic illustrating data queries, according to exemplaryembodiments. Once the monitoring service 26 is established, themonitoring server 20 sends a data query 70 to the query address 64identified in the profile 34. The data query 70 routes along thecommunications network (illustrated as reference numeral 42 in FIG. 2)to the query address 64 associated with the process server 40 and/or thedata server 55 that stores the desired data 22. When the data query 70is received, the data 22 is retrieved that is associated with the deviceidentifier 62. The data 22 is sent in a response 72. The response 72routes along the communications network 42 to the network addressassociated with the monitoring server 20. The monitoring server 20stores the data 22 for analysis. As earlier paragraphs explained,though, either the process server 40 and/or the data server 55 mayasynchronously the data 22 without being queried.

FIG. 5 is another schematic illustrating the monitoring service 26,according to exemplary embodiments. Now that the data 22 is retrieved,the monitoring server 20 determines if the data 22 is within limitsdefined by the user. The monitoring server 20 queries the profile 34 forthe user's self-defined rule 30 and/or threshold 32. Because the data 22is personal, the user defines and constructs the rules 30 and thresholds32. Some users may be avid runners, so they do not care if their heartrate exceeds 100 beats per minute. Other users, though, may be alarmedwhen their heart rate exceeds 100 beats per minute. The user may thuspersonalize the rule 30 and threshold 32 according to their desires,history, and activities. The user may also personalize the notificationaddress(es) 36 associated with each rule 30. The user may thus definewho gets the alert 28 when the data 22 lies out-of-bounds. Themonitoring server 20 compares the data 22 to the user's self-definedrule 30 and/or threshold 32. When the data 22 satisfies the threshold32, the rule 30 may require the alert 28 to the personalizednotification address 36 associated with the notification device 38.

FIG. 6 is a schematic illustrating a client interface 80 for themonitoring service 26, according to exemplary embodiments. The clientinterface 80 allows the user, from any client device 82, to configurethe monitoring service 26. FIG. 6 illustrates the client interface 80 asa web page 84 generated or assembled by a web server 86. When, forexample, the user wishes to access her profile 34, the client device 82sends a request to the web server 86. The monitoring server 20 and theweb server 86 cooperate to generate the client interface 80. The webserver 86 may transform the client interface 80 into the web page 84.The web server 86 sends the web page 84 to the client device 82, and theweb page 84 includes information describing the client interface 80. Themonitoring server 20, however, may optionally send the client interface80 directly to the network address associated with the client device 82.Regardless, the client interface 80 allows the user, at the clientdevice 82, to configure the monitoring service 26. The client interface80, for example, may be a digital dashboard that displays the user'spersonalized profile 34. The user may thus make inputs that change thedevice identifier 62, the query address 64, the rule 30, the threshold32, and/or the notification address 36. Whatever changes are made, theprofile 34 is updated in the database 56 of profiles.

FIG. 7 is a schematic illustrating a service ecosystem, according toexemplary embodiments. Here a wireless residential gateway 90 in aresidential network 92 sends environmental data 22 to the monitoringserver 20. The user's profile 34 defines what data 22 is collected andstored. The monitoring server 20 then monitors the data 22, according tothe user's profile 34. The user configures the profile 34 to determinehow the data 22 is processed. Moreover, the user normalizes the data 22by establishing the rules 30 and thresholds 32 for interpreting normalor abnormal conditions. The monitoring service 26 may provide templatesfor the user to configure with default values. However, the user mayconfigure their own data limits that determine when the alert 28 issent. The client interface 80 also allows the user to identify thenotification addresses 36 of the recipients. The user thus selects whatdata 22 is analyzed, how that data 22 is normalized, and what data 22requires the alert 28 and/or distribution. Exemplary embodiments thusdescribe a simple solution for complex monitoring. Because themonitoring service 26 is centralized and cloud-based, the user mayspecify the collection of the data 22 from any networked device, server,of third party service provider.

FIGS. 8-9 are schematics further illustrating the personalized rules 30,according to exemplary embodiments. Because the rules 30 and thresholds32 may be personalized, here the user may define a range 90 of the data22 for normal and/or abnormal conditions. FIG. 8, for example, againillustrates the profile 34 stored in the database 56 of profiles. Thetable 60 may include entries that associate the range 90 of the data 22to the device identifier 62 and to the rule 30. As the monitoringalgorithm 52 executes the rule 30, the monitoring algorithm 52 retrievesthe corresponding user-defined range 90 of the data 22. The processor 50compares the data 22 to the range 90 that is personalized by the user'sprofile 34. When the data 22 satisfies the range 90, the profile 34 mayalso include an entry defining the action 58 to implement. The action58, for example, may require retrieving a list 92 of the notificationaddresses 36. The monitoring algorithm 52 may thus cause thenotification 28 to be sent to each recipient in the list 92 of thenotification addresses 36. The monitoring algorithm 52 may also store,maintain, and update a log 94 of each alert 28 sent to each notificationaddress 36, perhaps according to date and time.

FIG. 9 illustrates a label 96 for the range 90 of the data 22. Here theuser may also define the label 96 that is associated with thecorresponding range 90 of the data 22. When the user defines the range90 of the data 22, the user may also associate her desired label 96 inthe profile 34. The label 96 thus helps discern the data 22 and/or therange 90 of the data 22. When the monitoring server 20 determines thatthe data 22 satisfies the range 90 of the data 22, the monitoringalgorithm 52 may cause the monitoring server 20 to retrieve and to sendthe label 96 to any recipient at the notification address(es) 36.

The label 96 may be explanatory. The label 96, in simple terms, may helpexplain to the recipient what data is out-of-bounds. The raw data 22 maybe complicated or even indecipherable to the recipient. The label 96,though, may present a simple textual or visual description of the data22, such as “heart rate,” “house temp,” or “location of Mom's phone.”The label 96 may even be more descriptive, such as “heart rate is toohigh” or “water in basement.” The user may thus define the label 96 tosummarize the out-of-bounds data 22. The user may thus map a range ofvalues of the data 22 to the label 96 for a simpler visual display thatis more meaningful to the user. The display label 96 helps the user tovisualize and categories the actual values of the data 22. For example,the user may map values of “0 to 9.99” as “Low,” data values “10 to14.99” as “Medium,” and data values “15 to 18.99” as “High.” Indeed, theuser may even establish the label 96 as “Very High” for values of “19 orgreater.” In this example, the rule 30 may thus specify two (2)instances of “High” values, and the threshold is 19.

FIG. 10 is a schematic illustrating multiple data ranges, according toexemplary embodiments. Here the user may define multiple ranges 90 ofthe data 22 from the same device identifier 62. The user may configurethe profile 34 with different rules 30 that are applied to the same ordifferent sets of the data 22. Each different rule 30 may have its owncorresponding range 90 of the data 22. The monitoring server 20retrieves and compares the multiple ranges 90 to the data 22 associatedwith the device identifier 62. When the data 22 satisfies any one of theranges 90 of the data 22, the monitoring server 20 retrieves thecorresponding label 96 and the action 58. The monitoring algorithm 52instructs the processor 50 to execute the action 58, such as sending thelabel 96 in the alert 28 to each of the notification addresses 36.Exemplary embodiments may thus permit different actions 58 based onwhich range 90 of the data 22 is satisfied.

Exemplary embodiments thus permit the user to completely define theactionable rules 30. Each rule 30 may be constructed from the entries inthe user's profile 34. The user may separately define the ranges 90 ofthe data 22 that are actionable. Different recipients may be assigned toeach range 90 of the data 22, and the corresponding label 96 helpssummarize the alert 28 for ease of explanation.

FIGS. 11-13 are graphical illustrations of the client interface 80,according to exemplary embodiments. FIGS. 11-13 illustrates the clientinterface 80 as a graphical user interface visually displayed on anydisplay device. The client interface 80 allows the user to view statusand summary of the monitored data 22. Data within tolerances may bereported as normal, while the data outside the user-defined ranges maybe labeled as desired by the user. FIG. 12, for example, illustrates thelabel 96 that is retrieved and sent in a message to the recipients atthe notification addresses 36. The label 96 provides a personalizeddescription of the data 22, the actual data values, and/or the rangethat triggered the alert 28. FIG. 13 illustrates how the profile 34 maybe configured to escalate alerts to help ensure corrective action istaken.

FIGS. 14-15 are more schematics illustrating the profile 34, accordingto exemplary embodiments. Here the user may configure the profile 34 todistinguish the data 22 according to frequency 100. Some devices thatgenerate the data 22 have multi-channel communications capabilities.That is, some devices may have the capability to send or receive dataover different channels or mediums. Some smart phones, for example, maysend the data 22 over a cellular network, over a local WI-FI® network,and/or over a BLUETOOTH® network. As the monitoring server 20 collectsthe data 22, the user's profile 34 may distinguish between the cellularfrequencies, the WI-FI® frequencies, and the BLUETOOTH® frequencies.That is, the user may configure her profile 34 to separately analyze thedata 22 sent over the cellular network from the data 22 sent over theWI-FI® network and from the BLUETOOTH® network. The rule 30 may thusspecify the frequencies 100 associated with the data 22. For example, asFIG. 14 illustrates, a rule 30 may specify that only the data 22associated with GSM cellular frequencies is analyzed and compared to thecorresponding range 90. A different WI-FI® rule 30 may specify that onlythe data 22 associated with WI-FI® frequencies is retrieved andanalyzed. The user may thus also define the corresponding range 90 andthe corresponding label 96 and action 58. If either rule 30 issatisfied, the monitoring algorithm 52 instructs the processor 50 toexecute the corresponding action 58.

As FIG. 14 illustrates, the label 96 helps distinguish the multi-channelcommunications capabilities. Because the same device identifier 62 mayhave different sets of the data 22, and thus the corresponding rules 30and ranges 90, the monitoring service 26 could produce confusing alerts28. A single device, in other words, could produce different alerts 28using the same device identifier 62. Exemplary embodiments help avoidthis confusion by establishing different labels 96 for differentfrequency spectrums. The user may define the label 96 to use differentnomenclature terms according to different frequency spectrums. Indeed,some devices may generate the data 22 using any of many differentfrequency spectrums, such as GSM®, CDMA®, TDMA®, WI-FI®, BLUETOOTH®, andmany others. The user may thus configure her profile 34 to separatelyanalyze the data 22 according to the frequency 100, and the label 96 maybe defined to identify the analysis by the frequency 100.

FIG. 15 illustrates logical associations to wireless access points,according to exemplary embodiments. Because the data 22 may bedistinguished according to the frequency 100, the user may optionallyconfigure the profile 34 with a wireless access point 102 thatwirelessly receives the data 22. Data 22 of different frequencies 100may have different wireless access points 102. A smart phones, forexample, may send the data 22 over cellular frequencies 100 to awireless access point 102 in a cellular network. However, if the smartphone sends the data 22 over a WI-FI® network, a different wirelessaccess point 102 may be used. Indeed, each different frequency 100 mayhave a different wireless access point 102. As the monitoring server 20collects the data 22, the user's profile 34 may distinguish between thefrequency 100 and/or the wireless access point 102. The profile 34 maythus be configured with different rules 30, ranges 90, labels 96, andnotification addresses 36 according to the frequency 100 and/or thewireless access point 102. If the corresponding rule 30 is satisfied,the monitoring algorithm 52 instructs the monitoring server 20 toexecute the corresponding action 58.

Exemplary embodiments may thus include a wireless data collector. Thiswireless data collector may monitor and detect transmissions of anyfrequency 100. The wireless data collector, for example, may have areceiver that receives transmissions in the WI-FI® and BLUETOOTH® rangeof frequencies. The receiver may be controlled by a processor thatexecutes an algorithm stored in memory. When the receiver detects atransmission, the processor may intercept a packet of data to read aheader and/or payload. The processor may then make a determination as towhether the transmission should be copied or forwarded to the monitoringserver 20 for analysis, as the above paragraphs explain. The wirelessdata collector may thus be deployed to pickup transmissions of desiredfrequencies, including USB ports. The wireless data collector mayinclude a microphone to translate sounds, such as high pitched loudalarms from smoke/CO2 detectors or burglar alarms, and to translatespoken phrases into telemetry data for sending to the monitoring server20.

FIGS. 16-17 are flowcharts illustrating a method or algorithm for datamonitoring, according to exemplary embodiments. A profile is retrievedthat is associated with an end-user of a monitoring service (Block 150).A device identifier is retrieved from the profile (Block 152). Thedevice identifier is sent in queries to processes operating in a cloudcomputing environment (Block 154). Data associated with the deviceidentifier is retrieved (Block 156). A user-defined rule (Block 158) andrange of data (Block 160) is retrieved from the profile. The data iscompared to the range of data (Block 162).

The flowchart continues with FIG. 17. When the data lies outside therange, an action is retrieved from the profile and executed (Block 164).A label may be retrieved from the profile (Block 166), along with a listof notification addresses (Block 168). The label is sent in a message toan address in the list of notification addresses (Block 170). Themessage is logged in a log (Block 172).

FIG. 18 is a schematic illustrating still more exemplary embodiments.FIG. 18 is a more detailed diagram illustrating a processor-controlleddevice 200. As earlier paragraphs explained, the monitoring algorithm 52may operate in any processor-controlled device. FIG. 18, then,illustrates the monitoring algorithm 52 stored in a memory subsystem ofthe processor-controlled device 200. One or more processors communicatewith the memory subsystem and execute either or both applications.Because the processor-controlled device 200 is well-known to those ofordinary skill in the art, no further explanation is needed.

FIG. 19 depicts still more operating environments for additional aspectsof the exemplary embodiments. FIG. 19 illustrates that the exemplaryembodiments may alternatively or additionally operate within otherprocessor-controlled devices 200. FIG. 19, for example, illustrates thatthe monitoring algorithm 52 may entirely or partially operate within aset-top box (“STB”) (202), a personal/digital video recorder (PVR/DVR)204, personal digital assistant (PDA) 206, a Global Positioning System(GPS) device 208, an interactive television 210, an Internet Protocol(IP) phone 212, a pager 214, a cellular/satellite phone 216, or anycomputer system, communications device, or any processor-controlleddevice utilizing a digital signal processor (DP/DSP) 218. Theprocessor-controlled device 200 may also include watches, radios,vehicle electronics, clocks, printers, gateways, mobile/implantablemedical devices, and other apparatuses and systems. Because thearchitecture and operating principles of the variousprocessor-controlled devices 200 are well known, the hardware andsoftware componentry of the various processor-controlled devices 200 arenot further shown and described.

Exemplary embodiments may be physically embodied on or in acomputer-readable storage medium. This computer-readable medium, forexample, may include CD-ROM, DVD, tape, cassette, floppy disk, opticaldisk, memory card, memory drive, and large-capacity disks. Thiscomputer-readable medium, or media, could be distributed toend-subscribers, licensees, and assignees. A computer program productcomprises processor-executable instructions for personalized monitoringof data, as the above paragraphs explained.

While the exemplary embodiments have been described with respect tovarious features, aspects, and embodiments, those skilled and unskilledin the art will recognize the exemplary embodiments are not so limited.Other variations, modifications, and alternative embodiments may be madewithout departing from the spirit and scope of the exemplaryembodiments.

The invention claimed is:
 1. A method, comprising: retrieving, by aserver, identifiers associated with devices, the devices commonlyassociated with a user; determining, by the server, Internet protocoladdresses associated with the devices commonly associated with the user;sending, by the server, queries via a communications network to thedevices associated with the Internet protocol addresses, the queriesrequesting data from the devices; receiving, by the server, responsessent via the communications network from the devices associated with theInternet protocol addresses, the responses comprising the data requestedby the queries; generating, by the server, a combined value of the datareceived from the devices; and executing, by the server, an action inresponse to the combined value of the data.
 2. The method of claim 1,further comprising determining a range associated with the combinedvalue of the data.
 3. The method of claim 1, further comprisingcombining the data according to a time.
 4. The method of claim 1,further comprising combining the data according to a percentage.
 5. Themethod of claim 1, further comprising retrieving an historicalcombination of the data.
 6. The method of claim 5, further comprisingcomparing the combined value of the data to the historical combination.7. The method of claim 1, further comprising determining that thecombined value of the data lies outside a range.
 8. A system,comprising: a hardware processor; and a memory device, the memory devicestoring instructions, the instructions when executed causing thehardware processor to perform operations, the operations comprising:retrieving identifiers associated with devices, the devices commonlyassociated with a user; determining Internet protocol addressesassociated with the devices commonly associated with the user; sendingqueries via a communications network to the devices associated with theInternet protocol addresses, the queries requesting data from thedevices; receiving responses sent via the communications network fromthe devices associated with the Internet protocol addresses, theresponses comprising the data requested by the queries; generating acombined value of the data received from the devices; and executing anaction in response to the combined value of the data.
 9. The system ofclaim 8, wherein the operations further comprise determining a rangeassociated with the combined value of the data.
 10. The system of claim8, wherein the operations further comprise combining the data accordingto a time.
 11. The system of claim 8, wherein the operations furthercomprise combining the data according to a percentage.
 12. The system ofclaim 8, wherein the operations further comprise retrieving anhistorical combination of the data.
 13. The system of claim 12, whereinthe operations further comprise comparing the combined value of the datato the historical combination.
 14. The system of claim 8, wherein theoperations further comprise determining that the combined value of thedata lies outside a range.
 15. A memory device storing instructions thatwhen executed cause a hardware processor to perform operations, theoperations comprising: retrieving identifiers associated with devices,the devices commonly associated with a user; determining Internetprotocol addresses associated with the devices commonly associated withthe user; sending queries via a communications network to the devicesassociated with the Internet protocol addresses, the queries requestingdata from the devices; receiving responses sent via the communicationsnetwork from the devices associated with the Internet protocoladdresses, the responses comprising the data requested by the queries;generating a combined value of the data received from the devices; andexecuting an action in response to the combined value of the data. 16.The memory device of claim 15, wherein the operations further comprisedetermining a range associated with the combined value of the data. 17.The memory device of claim 15, wherein the operations further comprisecombining the data according to a time.
 18. The memory device of claim15, wherein the operations further comprise combining the data accordingto a percentage.
 19. The memory device of claim 15, wherein theoperations further comprise retrieving an historical combination of thedata.
 20. The memory device of claim 19, wherein the operations furthercomprise comparing the combined value of the data to the historicalcombination.